Function reference • santaR

Example dataset

Acute inflammation

acuteInflammation: Measurement of 22 inflammatory mediators across time

Quick start

Functions designed for graphical or automated analysis

santaR-package santaR SANTAR: santaR: A package for Short AsyNchronous Time-series Analysis in R

santaR_start_GUI(): santaR Graphical User Interface

santaR_auto_fit(): Automate all steps of santaR fitting, Confidence bands estimation and p-values calculation for one or multiple variables

santaR_auto_summary(): Summarise, report and save the results of a santaR analysis

santaR_plot(): Plot a SANTAObj

Exported Functions

Functions exported by santaR

santaR_fit(): Generate a SANTAObj for a variable

santaR_CBand(): Compute Group Mean Curve Confidence Bands

santaR_pvalue_dist(): Evaluate difference in group trajectories based on the comparison of distance between group mean curves

santaR_pvalue_fit(): Evaluate difference in group trajectories based on the comparison of model fit (F-test) between one and two groups

santaR_pvalue_dist_within(): Evaluate difference between a group mean curve and a constant model

santaR_pvalue_fit_within(): Evaluate difference between a group mean curve and a constant model using the comparison of model fit (F-test)

Internals

Internal functions and helpers

AICc_smooth_spline(): Calculate the Akaike Information Criterion Corrected for small observation numbers for a smooth.spline

AIC_smooth_spline(): Calculate the Akaike Information Criterion for a smooth.spline

BIC_smooth_spline(): Calculate the Bayesian Information Criterion for a smooth.spline

get_eigen_DF(): Compute the optimal df and weighted-df using 5 spline fitting metric

get_eigen_DFoverlay_list(): Plot for each eigenSpline the automatically fitted spline, splines for all df and a spline at a chosen df

get_eigen_spline(): Compute eigenSplines across a dataset

get_eigen_spline_matrix(): Generate a Ind x Time + Var data.frame concatenating all variables from input variable

get_grouping(): Generate a matrix of group membership for all individuals

get_ind_time_matrix(): Generate a Ind x Time DataFrame from input data

get_param_evolution(): Compute the value of different fitting metrics over all possible df for each eigenSpline

loglik_smooth_spline(): Calculate the penalised loglikelihood of a smooth.spline

plot_nbTP_histogram(): Plot an histogram of the number of time-trajectories with a given number of time-points

plot_param_evolution(): Plot the evolution of different fitting parameters across all possible df for each eigenSpline

scaling_mean(): Mean scaling of each column

scaling_UV(): Unit-Variance scaling of each column